Nonlinear Granger Causality Research Articles

Nexus between green bonds, financial and environmental indicators

This study uses a novel perspective to examine the causal connectedness between green bonds and other conventional assets, including clean energy, price of CO2 emission allowances, Bitcoin, and the S&P 500 stock market covering from January 2013 to March 2019. We apply the Multilayer Perceptron Neural Network Non-linear Granger causality and Transfer Entropy to detect possible changes in the causal direction between green bonds and other considered variables. We find a bidirectional relationship between green bonds, S&P 500, and Bitcoin markets, while green bonds have a unidirectional connection with the price of CO2 emission allowances.

Causality indices for bivariate time series data: A comparative review of performance

Inferring nonlinear and asymmetric causal relationships between multivariate longitudinal data is a challenging task with wide-ranging application areas including clinical medicine, mathematical biology, economics, and environmental research. A number of methods for inferring causal relationships within complex dynamic and stochastic systems have been proposed, but there is not a unified consistent definition of causality in the context of time series data. We evaluate the performance of ten prominent causality indices for bivariate time series across four simulated model systems that have different coupling schemes and characteristics. Pairwise correlations between different methods, averaged across all simulations, show that there is generally strong agreement between methods, with minimum, median, and maximum Pearson correlations between any pair (excluding two similarity indices) of 0.298, 0.719, and 0.955, respectively. In further experiments, we show that these methods are not always invariant to real-world relevant transformations (data availability, standardization and scaling, rounding errors, missing data, and noisy data). We recommend transfer entropy and nonlinear Granger causality as particularly strong approaches for estimating bivariate causal relationships in real-world applications. Both successfully identify causal relationships and a lack thereof across multiple simulations, while remaining robust to rounding errors, at least 20% missing data and small variance Gaussian noise. Finally, we provide flexible open-access Python code for computation of these methods and for the model simulations.

Linear and nonlinear linkage of conditional stochastic volatility of interbank interest rates: Empirical evidence of the BRICS countries

The purpose of this article is to detect a possible linear and nonlinear causal relationship between the conditional stochastic volatility of log return of interbank interest rates for the BRICS countries in the period from January 2015 to October 2018. To extract the volatility of the analyzed time series, we use a stochastic volatility model with moving average innovations. To test a causal relationship between the estimated stochastic volatilities, two steps are applied. First, we used the Granger causality test and a vector autoregressive model (VAR). Secondly, we applied the nonlinear Granger causality test to the raw data to explore a new nonlinear causal relationship between stochastic volatility time series, and also applied it to the residual of the VAR model to confirm the causality detected in the first step. This study demonstrates the existence of some unidirectional/bidirectional linear/nonlinear causal relationships between the studied stochastic volatility time series.

The asymmetric contagion effect between stock market and cryptocurrency market

This paper studies asymmetric contagion effects between stock and cryptocurrency markets. We implement the time-varying symmetrized Joe-Clayton copula GARCH model and Bai-Perron breakpoint test to explore dynamic correlations between the daily log-returns of the two markets in each time range. The asymmetric contagion effects between the two markets are studied using the non-linear Granger causality test. We also find that the lower tail dependences are more significant than the upper ones. Our findings can be used as a reference for supervisory authorities, and also provide insights on risk hedging for rational investors to avoid underestimating risk when building their portfolios.

Asymmetric effects of premature deagriculturalization on economic growth and CO2 emissions: fresh evidence from Pakistan.

This paper examines the relationship between deagriculturalization, economic growth, and CO2 emissions in Pakistan from the period 1975 to 2018 by employing a nonlinear autoregressive distributed lag (NARDL) model and Granger causality approach. The asymmetric ARDL findings show that there is a significant negative relationship between agriculturalization and economic growth, while deagriculturalization does not induce economic growth in the long run in Pakistan. Moreover, agriculturalization and deagriculturalization have a negative significant effect on Pakistan’s carbon emissions in the long run. This study concludes that the asymmetric results deviate from symmetric results in Pakistan. The asymmetric causality test shows unidirectional asymmetric causality running from agriculturalization, deagriculturalization, and CO2 emissions. Moreover, agriculturalization and deagriculturalization do not Granger cause economic growth in Pakistan. Based on the results, the study stressed to formulate such policies which support economic growth and lower carbon emissions through reforming agriculture sector practices. These outcomes are very useful for Pakistan to formulate relevant policies.

Research on the Risk of the Online Lending Market in China: A New Perspective Based on MF-DCCA

ABSTRACT This paper examines the risk in the online lending market by analyzing the cross-correlation between formal (Shibor) and informal lending markets (including online and offline lending markets) in China using the multifractal detrended cross-correlation analysis (MF-DCCA) approach by employing time series data of interest rates covering the period from July 20, 2015, to January 17, 2020. The results reveal cross-correlations between Shibor and offline lending markets and between the online and offline lending markets in both the short and long terms. In addition, the nonlinear Granger causality test showed significant bidirectional causality between Shibor and the offline lending interest rate, and offline lending was the cause of online lending. No obvious causality relationships were noted between Shibor and online lending. The cross-correlation analysis for markets supported further strengthening of regulations of online lending markets.

THE ASYMMETRIC EFFECT OF OIL PRICE ON THE EXCHANGE RATE AND STOCK PRICE IN NIGERIA

The study examines the asymmetric effect of oil price on the exchange rate and stock price using the Nonlinear Autoregressive Distributive Lag (NARDL) technique on the time-series data spanning from January 1996 to September 2020. The multivariate cointegration test showed evidence of a long-run relationship among the stock price, exchange rate, and oil price. The linear Granger causality test showed that stock price is granger caused by oil price and exchange rate, and oil price is granger cause by stock price and exchange rate. The nonlinear granger causality showed evidence of nonlinearity using the BDS test. The Dick-Panchenko non-parametric and nonlinear Granger causality test in a contrary to the linear Granger causality test showed a unidirectional nonlinear causality from exchange rate to stock price at 10% level, and from oil price to exchange rate at 1% and 10% levels respectively. The result from the nonlinear ARDL revealed that change in oil price impacted asymmetrically on the exchange rate and stock price both in the short-run and long-run. The study recommends that the revenue generated from increasing oil price should be used for developing and reinstalling decayed infrastructure and oil-exporting countries should develop mechanisms and strategies that will ensure fair stability in the capital markets irrespective of the shocks in oil price.

Multiscale Systemic Risk and Its Spillover Effects in the Cryptocurrency Market

Since the advent of Bitcoin, the cryptocurrency market has become an important financial market. However, due to the existence of the cryptocurrency bubble, investors face more difficulties in risk portfolios. We adopt wavelet packet decomposition, nonlinear Granger causality test, risk spillover network, and STVAR model; retain the mature research of multiscale systemic risk based on time and frequency; and thus extend systemic risk to different regimes. We found that when frequency is combined with regimes, the risk spillover center will undergo subversive changes in the long run. We also proposed that BTC will be more robust at extreme values (like longest and shortest periods), while cryptocurrencies with smaller market capitalization will be stronger in the medium term. At the same time, the recession period will also spur on it.

Uncertainty Due to Infectious Diseases and Stock–Bond Correlation

We study the non-linear causal relation between uncertainty-due-to-infectious-diseases and stock–bond correlation. To this end, we use high-frequency 1-min data to compute daily realized measures of correlation and jumps, and then, we employ a nonlinear Granger causality test with the use of artificial neural networks so as to investigate the predictability of this type of uncertainty on realized stock–bond correlation and jumps. Our findings reveal that uncertainty-due-to-infectious-diseases has significant predictive value on the changes of the stock–bond relation.

Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data

A key challenge to gaining insight into complex systems is inferring nonlinear causal directional relations from observational time-series data. Specifically, estimating causal relationships between interacting components in large systems with only short recordings over few temporal observations remains an important, yet unresolved problem. Here, we introduce large-scale nonlinear Granger causality (lsNGC) which facilitates conditional Granger causality between two multivariate time series conditioned on a large number of confounding time series with a small number of observations. By modeling interactions with nonlinear state-space transformations from limited observational data, lsNGC identifies casual relations with no explicit a priori assumptions on functional interdependence between component time series in a computationally efficient manner. Additionally, our method provides a mathematical formulation revealing statistical significance of inferred causal relations. We extensively study the ability of lsNGC in inferring directed relations from two-node to thirty-four node chaotic time-series systems. Our results suggest that lsNGC captures meaningful interactions from limited observational data, where it performs favorably when compared to traditionally used methods. Finally, we demonstrate the applicability of lsNGC to estimating causality in large, real-world systems by inferring directional nonlinear, causal relationships among a large number of relatively short time series acquired from functional Magnetic Resonance Imaging (fMRI) data of the human brain.

Sectoral Nonlinear Causality Between Stock Market Volatility and the COVID-19 Pandemic: Evidence From India

This paper examines the linear and nonlinear relationship between daily confirmed COVID-19 cases and sectoral stock market volatility in India. The linear Granger causality test reveals bidirectional causality. Further, we observe that bidirectional nonlinear Granger causality exists between stock market volatility and COVID-19. This implies that the historical and lagged information can have a significant role in predicting COVID-19 cases and the stock market.

The Locally Gaussian Partial Correlation

It is well known in econometrics and other fields that the dependence structure for jointly Gaussian variables can be fully captured using correlations, and that the conditional dependence structure in the same way can be described using partial correlations. The partial correlation does not, however, characterize conditional dependence in many non-Gaussian populations. This article introduces the local Gaussian partial correlation (LGPC), a new measure of conditional dependence. It is a local version of the partial correlation coefficient that characterizes conditional dependence in a large class of populations. It has some useful and novel properties besides: The LGPC reduces to the ordinary partial correlation for jointly normal variables, and it distinguishes between positive and negative conditional dependence. Furthermore, the LGPC can be used to study departures from conditional independence in specific parts of the distribution. We provide several examples of this, both simulated and real, and derive estimation theory under a local likelihood estimation framework. Finally, we indicate how the LGPC can be used to construct a powerful test for conditional independence, which, for example, can be used to detect nonlinear Granger causality in time series.

Performance comparison of coupling-evaluation methods in discriminating between pregnancy and labor EHG signals

BackgroundRecent years have seen an increased interest in electrohysterogram (EHG) signals as a means to evaluate the synchronization of uterine contractions. Several studies have pointed out that the quality of signal processing – and hence the interpretation of measurement results – is affected significantly by the choice of measurement technique and the presence of non-stationary frequency content in EHG signals. To our knowledge, the effect of time variance on the quality of EHG signal processing has never been fully investigated. How best to process EHG signals with the goal of distinguishing labor-induced contractions from their harmless, pre-labor cousins, remains an open question. MethodOur methodology is based on three pillars. The first consists of a new method for EHG preprocessing in which we apply a second-order Butterworth filter to retain only the EHG fast-wave, low-frequency band (FWL), then use a bivariate piecewise stationary pre-segmentation (bPSP) algorithm to segment the EHG signal into stationary parts. The second pillar addresses the estimation of connectivity and directionality using three methods: nonlinear correlation coefficient (h2), general synchronization (H), and Granger causality (GC). The third pillar is related to signal classification and discrimination between pregnancy and labor using receiver operating curves (ROC) and connectivity and direction maps. For this purpose, we analyze the impact of four factors on data processing efficiency: i) method of connectivity detection, ii) effect of piecewise stationary segmentation preprocessing, iii) retained frequency content and iv) electrode configuration used for EHG recording (bipolar vs. unipolar). ResultsOur results show that piecewise signal segmentation and filtering considerably improves classification performance and statistical significance for some connectivity methods, in particular the h2. To this end we propose a new approach (detailed below) for h2 called Filtered-Windowed (FW) h2 that better highlights the differences between pregnancy and labor in the connectivity matrix and directionality maps. ConclusionsThis is the first comparative study of the effects of multiple processing factors on connectivity measurement efficiency. Our results indicate that appropriate preprocessing can improve the differentiation of pregnancy and labor-induced contraction signals and may lead to innovative applications in the prevention of preterm labor.

Risk spillovers and hedge strategies between global crude oil markets and stock markets: Do regime switching processes combining long memory and asymmetry matter?

This paper investigates risk spillovers and hedge strategies between global crude oil markets and stock markets. In the paper, we propose a multivariate long memory and asymmetry GARCH framework that integrates state-dependent regime switching in the mean process with multivariate long memory and asymmetry GARCH in the variance process. Our results first show that there are linear risk spillovers running from the US stock markets to the WTI oil market in the short term. However, the linear risk spillover effect running from the oil market to the US stock market can only exist in the long term. In addition, there is a bidirectional linear risk spillover effect between the European stock markets and the Brent oil market in the short and long terms. Furthermore, there is no linear risk spillover effect between the Dubai oil market and the Chinese stock market. Second, the nonlinear risk spillovers running from the WTI oil market to the US stock market can be found in the tranquil regime. Moreover, there is also a nonlinear risk spillover effect running from the European stock markets to the Brent oil market in the tranquil regime. In addition, the nonlinear risk spillover effect running from the Brent oil markets to the European stock market can be found in the crisis regime. Furthermore, there is bidirectional nonlinear Granger causality between the Dubai crude oil market and the Chinese stock market in the tranquil regime. Finally, dynamic hedge effectiveness shows that the regime switching process combined with long memory and asymmetry behavior seems to be a plausible and feasible way to conduct hedge strategies between the global crude oil markets and stock markets.

Asymmetric causality among carbon emission from agriculture, energy consumption, fertilizer, and cereal food production – A nonlinear analysis for Pakistan

Pakistan is an agricultural country where cereal crops are used as a staple food, but with time trend cereal production is decreasing. Therefore, this study aims to investigate asymmetric causality between agricultural carbon emissions, energy consumption, fertilizer consumption, and cereal food production in Pakistan. The secondary time series data over the period from1976 to 2018 was used to estimate the nonlinear-autoregressive distributed lag model. The empirical results of the linear Granger causality test confirm that the causality is running from energy consumption and fertilizer to cereal food production. The nonlinear Granger causality test declares cereal food production Granger cause to agricultural carbon emissions and energy consumption. It also confirms the unidirectional causality running from fertilizer consumption to cereal food production. Furthermore, the results of the nonlinear-autoregressive distributed lag model disclose that the positive and negative change in agricultural carbon emission, energy consumption, and fertilizer causes to changes in cereal food production. The dynamic multiplier curve suggests that positive and negative shocks influence cereal food production. The stability of the model was confirmed by the nonlinear-autoregressive distributed lag cumulative sum and cumulative sum of square test. Therefore this study suggests that it is essential for Pakistani farmers to switch from chemical fertilizer, burning non-renewable energy to organic fertilizer, and renewable energy in order to reduce carbon emission and increase cereal food production with a healthy environment.

Systemic financial risk early warning of financial market in China using Attention-LSTM model

We propose an Attention-LSTM neural network model to study the systemic risk early warning of China. Based on text mining, the network public opinion index is constructed and used as a training set to be incorporated into the early warning model to test the early warning effect. The results show that: (i) the network public opinion is the non-linear Granger causality of systemic risk. (ii) The Attention-LSTM neural network has strong generalization ability. Early warning effects have been significantly improved. (iii) Compared with the BP neural network model, the SVR model and the ARIMA model, the LSTM neural network early warning model has a higher accuracy rate, and its average prediction accuracy for systemic risk indicators has been improved over short, medium and long terms. When the attention mechanism is included in the LSTM, the Attention-LSTM neural network model is even more accurate in all the cases.

Exploring Asymmetric Nexus Between CO2 Emissions, Environmental Pollution, and Household Health Expenditure in China.

ObjectiveThis study investigates the nexus between household health expenditure, CO2 emissions and environmental pollution in China. We analyzed the asymmetric dynamic relationship between CO2 emissions, environmental pollution and household health expenditure for the period 1990 to 2019 in China.MethodsThis study adopted nonlinear autoregressive distributed lag (NARDL) and Granger causality following the diagnostic test. Furthermore, we applied Dickey–Fuller (ADF), PP unit root tests, Zivot and Andrews test for structural breaks in our analysis. The NARDL is the most suitable econometric technique for estimations, especially if the asymmetric relationship exists among the variables. NARDL technique is capable to explore the dynamic relationship between CO2 emissions, environmental pollution and household health expenditure.ResultsThe empirical results verify the asymmetric nexus between CO2 emissions, environmental pollution and household health expenditure in the context of China. The outcomes revealed that in the short run and long run positive shocks of CO2 emissions and environmental pollution positively affecting health expenditure, while negative shocks reduce health spendings. The results also demonstrate bi-directional causality among household health spendings, CO2 emissions and environmental pollution.ConclusionOur results support many previous studies, documenting that CO2 emissions positively contribute to the amount of household health expenditure, confirming the asymmetric relationship between CO2 emissions and household health expenditure. The results also confirm the statistically significant and asymmetrically positive relationship between environmental pollution and household health expenditure. This implies that Chinese residents have to bear more household health expenditure, in the case of more CO2 emissions and a greater amount of environmental pollution.

Research on the nonlinear dynamic relationship between FDI and CO2 emissions in the "One Belt, One Road" countries.

This study examines the relationship between FDI and CO2 emissions in a nonlinear framework using data from countries along the "One Belt, One Road" from 1979 to 2017 as the sample. First, the linear analysis method was used to examine the stability of FDI and CO2 emissions in countries along the "One Belt, One Road." In addition, the BDS (Brock-Dechert-Scheinkman test) method and nonlinear Granger causality test are used to investigate the nonlinear relationship between variables. Finally, a threshold vector autoregressive model (TVAR) is used to analyze the dynamic change mechanism between China's FDI and CO2 emissions, and a threshold vector error correction model (TVECM) is used to test how the variables respond to deviations from equilibrium. Then, the Markov switching model is used to test the robustness of the results. The research results show that China, India, South Africa, and other countries all have a nonlinear causal relationship between FDI and CO2 emissions. At the same time, the comovement of FDI and CO2 emissions in China has obvious structural break features, which are relevant for the underlying regime. Furthermore, the results also show that the adjustment process of the FDI toward equilibrium is highly persistent in the first regime, and CO2 emissions will adjust to an equilibrium state at a faster speed in the second regime. Therefore, this paper puts forward different policy suggestions for different countries. For China, we should pay attention to the long-term benefits of FDI and introduce high-tech green FDI.

Detection and root cause analysis of multiple plant-wide oscillations using multivariate nonlinear chirp mode decomposition and multivariate Granger causality

Plant-wide oscillation detection and root cause diagnosis are important for maintaining control performance. Existing methods are mainly limited to detecting single and time-invariant plant-wide oscillations. In this paper, a data-driven model combining multivariate nonlinear chirp mode decomposition (MNCMD) with multivariate Granger causality (MGC) is proposed to detect and analyze root causes for multiple plant-wide oscillations in process control system. First, an MNCMD-based detector is developed to capture the multiple plant-wide oscillations, where oscillating variables caused by different sources are automatically clustered into various groups. Then, MGC is applied to each group to obtain the root causes of multiple plant-wide oscillations. Compared with state-of-the-art detection methods, the proposed approach shows better performance in the following aspects: (i) ability to extract both single/multiple plant-wide oscillations; (ii) capability to process both time-invariant/time-varying oscillations and provide accurate time-frequency information. This work also outperforms original Granger causality and nonlinearity index-based method in providing clearer causal network. The effectiveness and advantages of the proposed approach are demonstrated with the help of both simulation and industrial case studies.

Energy-Growth Nexus in African Oil Exporting Countries: Evidence from Nonlinear Panel Cointegration and Granger Causality Tests

Energy-Growth Nexus in African Oil Exporting Countries: Evidence from Nonlinear Panel Cointegration and Granger Causality Tests